def func(x, xs, op):
op_py = OP_PY_MAP[op]
op_mpi = OP_MPI_MAP[op]
actual = np.empty_like(x)
comm.Allreduce(as_mpi(x), as_mpi(actual), op=op_mpi)
expected = op_py(xs)
assert_equal(actual, expected)
def func(x, xs, op):
op_py = OP_PY_MAP[op]
op_mpi = OP_MPI_MAP[op]
actual = np.empty_like(x)
comm.Allreduce(as_mpi(x), as_mpi(actual), op=op_mpi)
expected = op_py(xs)
assert_equal(actual, expected)
def func(nglobal):
d = np.array(comm.rank)
with filter_comm(comm.rank < nglobal, comm) as newcomm:
if newcomm is not None:
newcomm.Allreduce(MPI.IN_PLACE, as_mpi(d))
d = comm.bcast(d)
n = min(comm.size, nglobal)
assert d == n * (n - 1) // 2
def func(nglobal):
d = np.array(comm.rank)
with filter_comm(comm.rank < nglobal, comm) as newcomm:
if newcomm is not None:
newcomm.Allreduce(MPI.IN_PLACE, as_mpi(d))
d = comm.bcast(d)
n = min(comm.size, nglobal)
assert d == n * (n - 1) // 2
def get_hitmap(self, nside=None):
"""
Return a healpy map whose values are the number of times a pointing
hits the pixel.
"""
if nside is None:
nside = self.scene.nside
ipixel = self.sampling.healpix(nside)
npixel = 12 * nside**2
hit = np.histogram(ipixel, bins=npixel, range=(0, npixel))[0]
self.sampling.comm.Allreduce(MPI.IN_PLACE, as_mpi(hit), op=MPI.SUM)
return hit
def get_hitmap(self, nside=None):
"""
Return a healpy map whose values are the number of times a pointing
hits the pixel.
"""
if nside is None:
nside = self.scene.nside
ipixel = self.sampling.healpix(nside)
npixel = 12 * nside**2
hit = np.histogram(ipixel, bins=npixel, range=(0, npixel))[0]
self.sampling.comm.Allreduce(MPI.IN_PLACE, as_mpi(hit), op=MPI.SUM)
return hit
def get_center(self):
"""
Return the average pointing direction.
"""
if isscalarlike(self.masked) and self.masked:
n = 0
coords = np.zeros((1, 3))
else:
coords = self.cartesian
if not isscalarlike(self.masked):
valid = 1 - self.masked
coords *= valid[:, None]
n = np.sum(valid)
else:
n = len(self)
n = np.asarray(n)
center = np.sum(coords, axis=0)
self.comm.Allreduce(MPI.IN_PLACE, as_mpi(n))
self.comm.Allreduce(MPI.IN_PLACE, as_mpi(center))
if n == 0:
raise ValueError('There is no valid pointing.')
center /= n
return self.cartesian2spherical(center)
def get_center(self):
"""
Return the average pointing direction.
"""
if isscalarlike(self.masked) and self.masked:
n = 0
coords = np.zeros((1, 3))
else:
coords = self.cartesian
if not isscalarlike(self.masked):
valid = 1 - self.masked
coords *= valid[:, None]
n = np.sum(valid)
else:
n = len(self)
n = np.asarray(n)
center = np.sum(coords, axis=0)
self.comm.Allreduce(MPI.IN_PLACE, as_mpi(n))
self.comm.Allreduce(MPI.IN_PLACE, as_mpi(center))
if n == 0:
raise ValueError('There is no valid pointing.')
center /= n
return self.cartesian2spherical(center)
def test_diff():
shape = np.array((4,8,4))
for r in range(1,shape.size+1):
for axis in range(r):
a=np.random.random_integers(1, 10, size=shape[0:r]).astype(float)
MPI.COMM_WORLD.Bcast(as_mpi(a), root=0)
distribution = DistributionGlobalOperator(shape[0:r],
commout=MPI.COMM_WORLD)
for f in (u.diff, u.diffT, u.diffTdiff):
ref = a.copy()
f(ref, ref, axis=axis, comm=MPI.COMM_SELF)
reflocal = distribution(ref)
local = distribution(a)
dlocal = np.empty_like(local)
f(local, dlocal, axis=axis)
yield assert_equal, dlocal, reflocal, 'r={0}, axis={1}, f={2}, rank={3}, size={4}'.format(r, axis, f.__name__, rank, size)
f(local, local, axis=axis)
yield assert_equal, local, reflocal, 'r={0}, axis={1}, f={2}, rank={3}, size={4}'.format(r, axis, f.__name__, rank, size)
def test_diff():
shape = np.array((4, 8, 4))
for r in range(1, shape.size + 1):
for axis in range(r):
a = np.random.random_integers(1, 10, size=shape[0:r]).astype(float)
MPI.COMM_WORLD.Bcast(as_mpi(a), root=0)
distribution = DistributionGlobalOperator(shape[0:r],
commout=MPI.COMM_WORLD)
for f in (u.diff, u.diffT, u.diffTdiff):
ref = a.copy()
f(ref, ref, axis=axis, comm=MPI.COMM_SELF)
reflocal = distribution(ref)
local = distribution(a)
dlocal = np.empty_like(local)
f(local, dlocal, axis=axis)
yield assert_equal, dlocal, reflocal, 'r={0}, axis={1}, f={2}, rank={3}, size={4}'.format(
r, axis, f.__name__, rank, size)
f(local, local, axis=axis)
yield assert_equal, local, reflocal, 'r={0}, axis={1}, f={2}, rank={3}, size={4}'.format(
r, axis, f.__name__, rank, size)
